Code
library(tidyverse)
knitr::opts_chunk$set(echo = TRUE, warning=FALSE, message=FALSE)Challenge 2
challenge_2
railroads
faostat
hotel_bookings
Challenge Overview
Today’s challenge is to
- read in a data set, and describe the data using both words and any supporting information (e.g., tables, etc)
- provide summary statistics for different interesting groups within the data, and interpret those statistics
Read in the Data
Code
dataset <- read_csv('_data/hotel_bookings.csv')
dataset# A tibble: 119,390 × 32
hotel is_ca…¹ lead_…² arriv…³ arriv…⁴ arriv…⁵ arriv…⁶ stays…⁷ stays…⁸ adults
<chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Resor… 0 342 2015 July 27 1 0 0 2
2 Resor… 0 737 2015 July 27 1 0 0 2
3 Resor… 0 7 2015 July 27 1 0 1 1
4 Resor… 0 13 2015 July 27 1 0 1 1
5 Resor… 0 14 2015 July 27 1 0 2 2
6 Resor… 0 14 2015 July 27 1 0 2 2
7 Resor… 0 0 2015 July 27 1 0 2 2
8 Resor… 0 9 2015 July 27 1 0 2 2
9 Resor… 1 85 2015 July 27 1 0 3 2
10 Resor… 1 75 2015 July 27 1 0 3 2
# … with 119,380 more rows, 22 more variables: children <dbl>, babies <dbl>,
# meal <chr>, country <chr>, market_segment <chr>,
# distribution_channel <chr>, is_repeated_guest <dbl>,
# previous_cancellations <dbl>, previous_bookings_not_canceled <dbl>,
# reserved_room_type <chr>, assigned_room_type <chr>, booking_changes <dbl>,
# deposit_type <chr>, agent <chr>, company <chr>, days_in_waiting_list <dbl>,
# customer_type <chr>, adr <dbl>, required_car_parking_spaces <dbl>, …The dataset of hotel_booking describes the information like the room assigned, deposit type, reservation number, number of adults/children/babies across various countries.
Describe the data
Code
library(summarytools)
print(dfSummary(dataset))Data Frame Summary
dataset
Dimensions: 119390 x 32
Duplicates: 31994
-----------------------------------------------------------------------------------------------------------------------------------
No Variable Stats / Values Freqs (% of Valid) Graph Valid Missing
---- -------------------------------- -------------------------- ---------------------- ---------------------- ---------- ---------
1 hotel 1. City Hotel 79330 (66.4%) IIIIIIIIIIIII 119390 0
[character] 2. Resort Hotel 40060 (33.6%) IIIIII (100.0%) (0.0%)
2 is_canceled Min : 0 0 : 75166 (63.0%) IIIIIIIIIIII 119390 0
[numeric] Mean : 0.4 1 : 44224 (37.0%) IIIIIII (100.0%) (0.0%)
Max : 1
3 lead_time Mean (sd) : 104 (106.9) 479 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 69 < 737 :
IQR (CV) : 142 (1) : : .
: : : . .
4 arrival_date_year Mean (sd) : 2016.2 (0.7) 2015 : 21996 (18.4%) III 119390 0
[numeric] min < med < max: 2016 : 56707 (47.5%) IIIIIIIII (100.0%) (0.0%)
2015 < 2016 < 2017 2017 : 40687 (34.1%) IIIIII
IQR (CV) : 1 (0)
5 arrival_date_month 1. August 13877 (11.6%) II 119390 0
[character] 2. July 12661 (10.6%) II (100.0%) (0.0%)
3. May 11791 ( 9.9%) I
4. October 11160 ( 9.3%) I
5. April 11089 ( 9.3%) I
6. June 10939 ( 9.2%) I
7. September 10508 ( 8.8%) I
8. March 9794 ( 8.2%) I
9. February 8068 ( 6.8%) I
10. November 6794 ( 5.7%) I
[ 2 others ] 12709 (10.6%) II
6 arrival_date_week_number Mean (sd) : 27.2 (13.6) 53 distinct values . : . . . 119390 0
[numeric] min < med < max: . : : : : : : (100.0%) (0.0%)
1 < 28 < 53 . : : : : : : : : :
IQR (CV) : 22 (0.5) : : : : : : : : : :
: : : : : : : : : :
7 arrival_date_day_of_month Mean (sd) : 15.8 (8.8) 31 distinct values : 119390 0
[numeric] min < med < max: : : : . : : . : : (100.0%) (0.0%)
1 < 16 < 31 : : : : : : : : : :
IQR (CV) : 15 (0.6) : : : : : : : : : :
: : : : : : : : : :
8 stays_in_weekend_nights Mean (sd) : 0.9 (1) 17 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 1 < 19 :
IQR (CV) : 2 (1.1) : :
: :
9 stays_in_week_nights Mean (sd) : 2.5 (1.9) 35 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 2 < 50 :
IQR (CV) : 2 (0.8) :
:
10 adults Mean (sd) : 1.9 (0.6) 14 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 2 < 55 :
IQR (CV) : 0 (0.3) :
:
11 children Mean (sd) : 0.1 (0.4) 0 : 110796 (92.8%) IIIIIIIIIIIIIIIIII 119386 4
[numeric] min < med < max: 1 : 4861 ( 4.1%) (100.0%) (0.0%)
0 < 0 < 10 2 : 3652 ( 3.1%)
IQR (CV) : 0 (3.8) 3 : 76 ( 0.1%)
10 : 1 ( 0.0%)
12 babies Mean (sd) : 0 (0.1) 0 : 118473 (99.2%) IIIIIIIIIIIIIIIIIII 119390 0
[numeric] min < med < max: 1 : 900 ( 0.8%) (100.0%) (0.0%)
0 < 0 < 10 2 : 15 ( 0.0%)
IQR (CV) : 0 (12.3) 9 : 1 ( 0.0%)
10 : 1 ( 0.0%)
13 meal 1. BB 92310 (77.3%) IIIIIIIIIIIIIII 119390 0
[character] 2. FB 798 ( 0.7%) (100.0%) (0.0%)
3. HB 14463 (12.1%) II
4. SC 10650 ( 8.9%) I
5. Undefined 1169 ( 1.0%)
14 country 1. PRT 48590 (40.7%) IIIIIIII 119390 0
[character] 2. GBR 12129 (10.2%) II (100.0%) (0.0%)
3. FRA 10415 ( 8.7%) I
4. ESP 8568 ( 7.2%) I
5. DEU 7287 ( 6.1%) I
6. ITA 3766 ( 3.2%)
7. IRL 3375 ( 2.8%)
8. BEL 2342 ( 2.0%)
9. BRA 2224 ( 1.9%)
10. NLD 2104 ( 1.8%)
[ 168 others ] 18590 (15.6%) III
15 market_segment 1. Aviation 237 ( 0.2%) 119390 0
[character] 2. Complementary 743 ( 0.6%) (100.0%) (0.0%)
3. Corporate 5295 ( 4.4%)
4. Direct 12606 (10.6%) II
5. Groups 19811 (16.6%) III
6. Offline TA/TO 24219 (20.3%) IIII
7. Online TA 56477 (47.3%) IIIIIIIII
8. Undefined 2 ( 0.0%)
16 distribution_channel 1. Corporate 6677 ( 5.6%) I 119390 0
[character] 2. Direct 14645 (12.3%) II (100.0%) (0.0%)
3. GDS 193 ( 0.2%)
4. TA/TO 97870 (82.0%) IIIIIIIIIIIIIIII
5. Undefined 5 ( 0.0%)
17 is_repeated_guest Min : 0 0 : 115580 (96.8%) IIIIIIIIIIIIIIIIIII 119390 0
[numeric] Mean : 0 1 : 3810 ( 3.2%) (100.0%) (0.0%)
Max : 1
18 previous_cancellations Mean (sd) : 0.1 (0.8) 15 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 0 < 26 :
IQR (CV) : 0 (9.7) :
:
19 previous_bookings_not_canceled Mean (sd) : 0.1 (1.5) 73 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 0 < 72 :
IQR (CV) : 0 (10.9) :
:
20 reserved_room_type 1. A 85994 (72.0%) IIIIIIIIIIIIII 119390 0
[character] 2. B 1118 ( 0.9%) (100.0%) (0.0%)
3. C 932 ( 0.8%)
4. D 19201 (16.1%) III
5. E 6535 ( 5.5%) I
6. F 2897 ( 2.4%)
7. G 2094 ( 1.8%)
8. H 601 ( 0.5%)
9. L 6 ( 0.0%)
10. P 12 ( 0.0%)
21 assigned_room_type 1. A 74053 (62.0%) IIIIIIIIIIII 119390 0
[character] 2. D 25322 (21.2%) IIII (100.0%) (0.0%)
3. E 7806 ( 6.5%) I
4. F 3751 ( 3.1%)
5. G 2553 ( 2.1%)
6. C 2375 ( 2.0%)
7. B 2163 ( 1.8%)
8. H 712 ( 0.6%)
9. I 363 ( 0.3%)
10. K 279 ( 0.2%)
[ 2 others ] 13 ( 0.0%)
22 booking_changes Mean (sd) : 0.2 (0.7) 21 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 0 < 21 :
IQR (CV) : 0 (2.9) :
:
23 deposit_type 1. No Deposit 104641 (87.6%) IIIIIIIIIIIIIIIII 119390 0
[character] 2. Non Refund 14587 (12.2%) II (100.0%) (0.0%)
3. Refundable 162 ( 0.1%)
24 agent 1. 9 31961 (26.8%) IIIII 119390 0
[character] 2. NULL 16340 (13.7%) II (100.0%) (0.0%)
3. 240 13922 (11.7%) II
4. 1 7191 ( 6.0%) I
5. 14 3640 ( 3.0%)
6. 7 3539 ( 3.0%)
7. 6 3290 ( 2.8%)
8. 250 2870 ( 2.4%)
9. 241 1721 ( 1.4%)
10. 28 1666 ( 1.4%)
[ 324 others ] 33250 (27.8%) IIIII
25 company 1. NULL 112593 (94.3%) IIIIIIIIIIIIIIIIII 119390 0
[character] 2. 40 927 ( 0.8%) (100.0%) (0.0%)
3. 223 784 ( 0.7%)
4. 67 267 ( 0.2%)
5. 45 250 ( 0.2%)
6. 153 215 ( 0.2%)
7. 174 149 ( 0.1%)
8. 219 141 ( 0.1%)
9. 281 138 ( 0.1%)
10. 154 133 ( 0.1%)
[ 343 others ] 3793 ( 3.2%)
26 days_in_waiting_list Mean (sd) : 2.3 (17.6) 128 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
0 < 0 < 391 :
IQR (CV) : 0 (7.6) :
:
27 customer_type 1. Contract 4076 ( 3.4%) 119390 0
[character] 2. Group 577 ( 0.5%) (100.0%) (0.0%)
3. Transient 89613 (75.1%) IIIIIIIIIIIIIII
4. Transient-Party 25124 (21.0%) IIII
28 adr Mean (sd) : 101.8 (50.5) 8879 distinct values : 119390 0
[numeric] min < med < max: : (100.0%) (0.0%)
-6.4 < 94.6 < 5400 :
IQR (CV) : 56.7 (0.5) :
:
29 required_car_parking_spaces Mean (sd) : 0.1 (0.2) 0 : 111974 (93.8%) IIIIIIIIIIIIIIIIII 119390 0
[numeric] min < med < max: 1 : 7383 ( 6.2%) I (100.0%) (0.0%)
0 < 0 < 8 2 : 28 ( 0.0%)
IQR (CV) : 0 (3.9) 3 : 3 ( 0.0%)
8 : 2 ( 0.0%)
30 total_of_special_requests Mean (sd) : 0.6 (0.8) 0 : 70318 (58.9%) IIIIIIIIIII 119390 0
[numeric] min < med < max: 1 : 33226 (27.8%) IIIII (100.0%) (0.0%)
0 < 0 < 5 2 : 12969 (10.9%) II
IQR (CV) : 1 (1.4) 3 : 2497 ( 2.1%)
4 : 340 ( 0.3%)
5 : 40 ( 0.0%)
31 reservation_status 1. Canceled 43017 (36.0%) IIIIIII 119390 0
[character] 2. Check-Out 75166 (63.0%) IIIIIIIIIIII (100.0%) (0.0%)
3. No-Show 1207 ( 1.0%)
32 reservation_status_date min : 2014-10-17 926 distinct values . : : : : 119390 0
[Date] med : 2016-08-07 : : : : : : . (100.0%) (0.0%)
max : 2017-09-14 . : : : : : : :
range : 2y 10m 28d : : : : : : : :
. : : : : : : : :
-----------------------------------------------------------------------------------------------------------------------------------We can see here that the dataset contains the data between the years 2015-2017 and there are 2 distinct values of the Resort and city in the dataset. Around 37% of the bookings are canceled.
Provide Grouped Summary Statistics
Code
dataGroupedByADT <- dataset %>%
group_by(arrival_date_month)
dataGroupedByADT %>%
summarise(
avg_stays_in_week_nights = mean(stays_in_week_nights, nr.rm = TRUE),
avg_stays_in_weekend_nights = mean(stays_in_weekend_nights, nr.rm = TRUE)
)# A tibble: 12 × 3
arrival_date_month avg_stays_in_week_nights avg_stays_in_weekend_nights
<chr> <dbl> <dbl>
1 April 2.42 0.949
2 August 2.84 1.06
3 December 2.36 0.865
4 February 2.18 0.853
5 January 2.19 0.779
6 July 2.81 1.12
7 June 2.66 0.920
8 March 2.56 0.859
9 May 2.41 0.870
10 November 2.40 0.785
11 October 2.23 0.895
12 September 2.52 0.942Explain and Interpret
The first table is grouped by the months when the booking is done and analyses how the average stay in the weekday and weekend night differs per month. Such sorts of analysis might help the hotel in organizing the staff and resources on the basis of the month of the year during the weekend and weekdays. If is also helpful in planning the activities that are seasonal specific during the weekdays along with deciding the prices of the room and the discounts applied to them.
Code
prop.table(table(select(dataGroupedByADT, reservation_status))) %>%
`*`(100) %>%
round(2) reservation_status
arrival_date_month Canceled Check-Out No-Show
April 3.72 5.50 0.06
August 4.31 7.24 0.08
December 1.92 3.69 0.07
February 2.07 4.50 0.19
January 1.45 3.45 0.06
July 3.89 6.63 0.08
June 3.73 5.36 0.07
March 2.54 5.57 0.10
May 3.81 5.96 0.11
November 1.71 3.91 0.07
October 3.50 5.79 0.06
September 3.39 5.35 0.06Explain and Interpret
The second table that shows the distribution in the percentage basis of the booking of the segments that were cancelled which helps analyse which month gets the highest and lowest number of cancellations. This would help the hotel coming up with the ways to reduce the number of cancellations in specific months.